Corporate Credit Rating based on Bankruptcy Probability Using AdaBoost Algorithm-based Support Vector Machine

Taeksoo Shin(신택수),Taeho Hong(홍태호) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.3

최근 몇 년간 SVM(support vector machines)기법은 패턴인식 또는 분류의사결정문제를 위한 분석기법으로서 기존의 데이터마이닝 기법과 비교할 때 매우 높은 성과를 갖는 것으로 인식되어 왔다. 더 나아나 많은 연구자들은 SVM기법이 1980년대 이후 대표적인 예측 및 분류모형으로 인정받은 인공신경망기법(ANNs : Artificial Neural Networks)에 비해 더 성과가 좋다는 사실을 실증적으로 입증해 왔다(Amendolia et al. 2003; Huang et al. 2004 Huang et al. 2005; Tay and Cao 2001; Min and Lee 2005; Shin et al. 2005; Kim 2003). 일반적으로 이와 같이 다양한 데이터마이닝 기법에 의해 분석되는 이진분류 또는 다분류 의사결정문제들은 특히 금융분야 등에 있어서 오분류비용에 민감하며 이로 인한 오분류의 경제적 손실도 상대적으로 매우 크다고 할 수 있다. 따라서 기업부도예측모형과 같은 이진분류모형의 결과값을 부도확률에 기초하여 정교하게 계산된 사후확률의 개념으로서 다분류의 신용등급평가의 문제로 변환할 필요가 있다. 그러나 SVM 모형의 결과값은 기본적으로 그와 같은 부도확률분포를 보여주지 않는다. 따라서 그러한 확률분포를 정교하게 보여줄 방법을 제시할 필요가 있다(Platt 1999; Drish 2001). 본 연구는 AdaBoost 알고리즘기반의 SVM 모형을 이용하여 이진분류모형으로서 IT 기업의 부실예측모형에 적용한 후 이 SVM 모형의 예측결과를 SVM의 손실함수에 적용하여 계산된 값을 사후부도확률의 정규분포 특성에 따라 이를 구간화하여 IT기업에 대한 다분류 신용등급 평가의 문제로 전환시키는 방법을 제시하였다. 그리고 본 연구에서 제안하는 방법은 이러한 AdaBoost 알고리즘기반 SVM 모형이 각 기업이 고유한 신용위험(부도확률)을 갖고 있다는 조건하에서 신용등급부여를 위한 부도확률분포 구간을 정교하게 조정함으로써 오분류 문제를 좀 더 줄일 수 있음을 제시하였다. Recently support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore many researches in particular have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al. 2003; Huang et al. 2004 Huang et al. 2005; Tay and Cao 2001; Min and Lee 2005; Shin et al. 2005; Kim 2003).The classification decision such as a binary or multi-class decision problem used by any classifier i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified a terrible economic loss for investors or financial decision makers may happen. Therefore it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt 1999; Drish 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk i.e. bankruptcy probability.

The Social Capital Effect on Knowledge Creation and its Relation to Organizational Performance in the Outsourced Contact Centers

Taeksoo Shin,Tan Jia Huey 한국경영정보학회 2012 한국경영정보학회 학술대회논문집 Vol.2012 No.1

선박 기인 대기오염물질 규제동향 및 대응방안

임택수(Taeksoo Lim),신영수(Youngsoo Shin),김인구(Ingu Kim),김차수(Chasu Kim),윤대전(Daejeon Yoon) 한국해양환경·에너지학회 2018 한국해양환경·에너지학회 학술대회논문집 Vol.2018 No.11

Analysis of Adolescent Obesity Topics Over Time Using Korean News Corpuses

Jihea Choi,Taeksoo Shin 한국간호과학회 2023 한국간호과학회 학술대회 Vol.2023 No.11

Evaluating the Quality of Recommendation System by Using Serendipity Measure

Dorjmaa Tserendulam,Taeksoo Shin 한국경영정보학회 2016 한국경영정보학회 학술대회논문집 Vol.2016 No.6

Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis

Tserendulam Dorjmaa,Taeksoo Shin 한국IT서비스학회 2017 한국IT서비스학회지 Vol.16 No.3

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie’s genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies’ network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie’s network. Those four centrality values and movies’ genre data were used to classify the movie popularity in this study. The logistic regression, neural network, naïve Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier’s performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie’s genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie’s genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis

Tserendulam Dorjmaa,Taeksoo Shin 한국경영학회 2015 한국경영학회 통합학술발표논문집 Vol.2015 No.08

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in many fields such as movie recommendation of e-commerce service. However, most of classification approaches for predicting movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie’s genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies’ network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie’s network. Those four centrality values and movies’ genre data were used to classify the movie popularity in this study. The logistic regression, neural network, naive Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier’s performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model has about 10% higher accuracy than other classification models. The implications of our results show that our proposed model could be used for improving movie popularity classification.

Internal Control Risk Assessment System Using CRAS-CBR

SungSik Hwang,Taeksoo Shin,Ingoo Han 한국지능정보시스템학회 2003 한국지능정보시스템학회 학술대회논문집 Vol.- No.-

Integrated Model of Data Mining and Sentiment Analysis for Daily KOSPI Forecasting

Zhongjun Cui,Taeksoo Shin 한국경영학회 2015 한국경영학회 통합학술발표논문집 Vol.2015 No.08

Although stock price forecasting has been a traditional topic in the research domain of investment decision making, there have been many difficulties in forecasting stock price due to the unexpected rapid changes in stock prices. Recently, many researchers attempted to analyze sentiment in SNS data or news data to forecast stock price, but these researches have limitations that they used only one of sentiment data or KOSPI (Korea Composite Stock Price index) data in forecasting stock price. The aim of this paper is to propose new domain-specific sentiment dictionaries on stock price by using sentiment analysis, and acquire daily sentiment indices by analyzing the sentiment of news articles, and then use both of the sentiment data and KOSPI data together as input for data mining model for daily KOSPI forecasting, and finally improve the accuracy of forecasting the direction of KOSPI. TF-IDF weight was considered in building sentiment dictionaries and calculating daily sentiment indices by using domain-specific sentiment dictionaries. Our empirical result showed that in particular, a K-NN model with KOSPI and the sentiment data calculated by using both TF-IDF weights-based sentiment dictionary and the weights of news article itself in each news article data, had the accuracy of 68% and outperformed any other models in validation data.

An Integrated Model of SNA and AI Methods for Predicting Serendipitous Items

Tserendulam(체렌돌람),Taeksoo Shin(신택수) 한국지능정보시스템학회 2018 한국지능정보시스템학회 학술대회논문집 Vol.2018 No.11